Enhancing cybersecurity through script development using machine and deep learning for advanced threat mitigation.

Area of Science:

• Computer Science
• Cybersecurity
• Artificial Intelligence

Background:

• Traditional cybersecurity measures are insufficient against complex modern threats.
• Software development security is a critical concern due to the increasing threat landscape.
• Machine Learning (ML) and Deep Learning (DL) offer advanced solutions for automated security analysis.

Purpose of the Study:

• To explore the application of ML and DL in enhancing cybersecurity for script development.
• To demonstrate the effectiveness of ML/DL techniques in improving software security benchmarks.
• To provide insights into integrating ML/DL into practical software development workflows.

Main Methods:

• Leveraged the Fashion MNIST dataset for empirical validation.
• Employed a Convolutional Neural Network (CNN) model for security task analysis.
• Included data preprocessing, model training, and performance evaluation using accuracy and loss metrics.

Main Results:

• The proposed ML/DL methodology demonstrated significant improvements in cybersecurity metrics.
• Empirical findings validated the efficacy of CNN models in enhancing script development security.
• The study confirmed the potential of ML and DL in reinforcing software security.

Conclusions:

• Integrating ML and DL techniques into script development bolsters software robustness against cyber threats.
• The research contributes valuable insights for practitioners aiming to enhance software resilience.
• ML/DL integration is a promising strategy for addressing evolving cybersecurity challenges in software development.